Simulation Budget Allocation for Selecting the Top-m Designs with Input Uncertainty

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

25 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)3127-3134
Journal / PublicationIEEE Transactions on Automatic Control
Volume63
Issue number9
Online published9 Jan 2018
Publication statusPublished - Sep 2018

Abstract

This paper considers the problem of selecting the top-m designs using simulation with input uncertainty. The performance of each design is measured by its worst case performance. The objective of this paper is to maximize the probability of correctly selecting the top-m designs given a fixed simulation budget. Due to the complexity of probability of correct selection (PCS), we develop a lower bound for the PCS and derive an asymptotically optimal budget allocation rule. Useful insights on characterizing the efficient budget allocation rule with input uncertainty are provided. Meanwhile, a sequential simulation procedure is suggested to implement the allocation rule. A series of numerical experiments indicate that the proposed simulation budget allocation rule can outperform all existing selection rules.

Research Area(s)

  • Algorithm design and analysis, Computational modeling, Data models, input uncertainty, OCBA, Optimization, random systems, ranking and selection, Resource management, simulation optimization, Uncertainty